Experimental investigation of a cook-off temperature in a hot barrel

The experimental investigations of the effect of contact time/temperature on initiating the cook-off using 7.62 mm calibre cartridge cases （CC） were conducted previously. These cartridges were filled with commercial off-the-shelf （COTS） double based （DB） propellant （Bulls Eye） and were loaded in a hot chamber. The thermal explosion temperature is of great significance to both weapon designers and safety inspectors as it provides the operational limit and safe operating temperature. For CC under test, it was found that the cook-off temperatures of this propellant were encountered with the heat transfer profile of the simulated gun barrel between 151.4 ℃ and 153.4 ℃, with a reaction occurring in less than 300 s after the round was chambered. Usefully, each experiment was found to be consistent and repeatable.     

Novel CO(2)/N(2)O waveguide laser

We present a new design for a waveguide laser which is easily constructed from readily available materials. We have demonstrated its operation for CO(2) (8.5-W output) and N(2)O (1.5-W output).     

Deep Multimodal Fusion: A Hybrid Approach

We propose a novel hybrid model that exploits the strength of discriminative classifiers along with the representation power of generative models. Our focus is on detecting multimodal events in time varying sequences as well as generating missing data in any of the modalities. Discriminative classifiers have been shown to achieve higher performances than the corresponding generative likelihood-based classifiers. On the other hand, generative models learn a rich informative space which allows for data generation and joint feature representation that discriminative models lack. We propose a new model that jointly optimizes the representation space using a hybrid energy function. We employ a Restricted Boltzmann Machines (RBMs) based model to learn a shared representation across multiple modalities with time varying data. The Conditional RBMs (CRBMs) is an extension of the RBM model that takes into account short term temporal phenomena. The hybrid model involves augmenting CRBMs with a discriminative component for classification. For these purposes we propose a novel Multimodal Discriminative CRBMs (MMDCRBMs) model. First, we train the MMDCRBMs model using labeled data by training each modality, followed by training a fusion layer. Second, we exploit the generative capability of MMDCRBMs to activate the trained model so as to generate the lower-level data corresponding to the specific label that closely matches the actual input data. We evaluate our approach on ChaLearn dataset, audio-mocap, as well as the Tower Game dataset, mocap-mocap as well as three multimodal toy datasets. We report classification accuracy, generation accuracy, and localization accuracy and demonstrate its superiority compared to the state-of-the-art methods.     

Performance measurement of product returns with recovery for sustainable manufacturing

Today's lifecycle of new and emerging products, increase in labour costs in developed countries and user's expectations or behaviours including frequently upgrading items with latest features, influence the growth rate of product disposal to landfill. To reduce the negative impact on the environment, global manufacturers need to take responsibility for designing sustainable products and implementing cleaner production systems for 3R operations (3R–Reuse/Remanufacture/Recycle). Nevertheless, there is still a lack of comprehensive measures for assessing product returns with recovery settings. In this paper, a framework for performance evaluation using design for six sigma methodology is developed to estimate utilisation value of a manufactured product with recovery settings, which accounts for total recovery cost, manufacturing lead-time, minimisation for landfill waste and quality characteristic. Finally, a numerical example based on these performance attributes to assess product utilisation value is presented.     

Exploring the fitness landscape and the run-time behaviour of an iterated local search algorithm for cost-based abduction

Cost-based abduction (CBA) is an important problem in reasoning under uncertainty, and can be considered a generalization of belief revision. CBA is known to be NP-hard and has been a subject of considerable research over the past decade. In this paper, we investigate the fitness landscape for CBA, by looking at fitness–distance correlation for local minima and at landscape ruggedness. Our results indicate that stochastic local search techniques would be promising on this problem. We go on to present an iterated local search algorithm based on hill-climbing, tabu search, and simulated annealing. We compare the performance of our algorithm to simulated annealing, and to Santos' integer linear programming method for CBA.     

Interfacial hybridization kinetics of oligonucleotides immobilized onto fused silica surfaces

Fused silica optical fibers have been used in an intrinsic mode optical configuration as biosensors for fluorescence based detection of hybridization of nucleic acids. In this work, the kinetics of hybridization of single-stranded oligonucleotides that were covalently immobilized were studied. The probe DNA was dT₂₀, and the target was Fluorescein-labeled non-complementary (dT₂₀) or complementary (dA₂₀) oligonucleotide. Chronofluorimetric monitoring of the adsorption and hybridization processes was used to investigate oligonucleotide films of different density, in different salt concentrations, at temperatures of 25 and 40 °C, with the concentration of the target DNA being 0.005–0.1 μM. Mathematical models based on first- and second-order Langmuir adsorption have been examined to describe both the adsorption and the hybridization processes. Experimental data were processed using the models, and the hybridization kinetics were calculated. Hybridization kinetics on these optical fiber DNA sensors was found to be up to three orders faster than results presented for a number of other experiments using different immobilization chemistries.     

A Generative System for Mamluk Madrasa Form-Making

In this paper, a parametric shape grammar for the derivation of the floor plans of educational buildings (madrasas) in Mamluk architecture is presented. The grammar is constructed using a corpus of sixteen Mamluk madrasas that were built in Egypt, Syria, and Palestine during the Mamluk period. Based on an epistemological premise of structuralism, the morphology of Mamluk madrasas is analyzed to deduce commonalities of the formal and compositional aspects among them. The set of underlying common lexical and syntactic elements that are shared by the study cases is listed. The shape rule schemata to derive Mamluk madrasa floor plans are formulated. The sets of lexical elements and syntactic rules are systematized to form a linguistic framework. The theoretical framework for the formal language of Mamluk architecture is structured to establish a basis for a computerized model for the automatic derivation of Mamluk madrasa floor plans.     

Understanding the behavior of compiler optimizations

Compiler optimizations are difficult to implement and add complexity to a compiler. For this reason, compiler writers are selective about implementing them: they implement only the ones that they believe will be beneficial. To support compiler writers in this, we describe a method for measuring the cost and benefits of compiler optimizations, both individually and in synergy with other optimizations. We demonstrate our method by presenting results for the optimizations implemented in the Jikes Research Virtual Machine on the PowerPC and IA32 platforms. Copyright © 2006 John Wiley & Sons, Ltd.     

Effect of the Te/Si ratio on the electrical characteristics of the amorphous chalcogenide switches

The effect of composition on amorphous chalcogenide threshold switches of the system Si _x Te_60-x As₃₀Ge₁₀, wherex = 5, 10, 12 and 20, has been studied. The compositionx = 5 shows the best switching characteristics, e.g. the smallest holding voltage (V _h = 0.4V), the highest ON state current (I _h = 45A) and smallest threshold voltage (V _s = 1.5V). Applying the three mechanisms of conductance of Mottet al. (Phil. Mag. 37 (1975) 961), it is found that for a particular composition ₂ < ₁ < ₀ (the pre-exponential factors) andW ₂ < (E _a –E ₁ +W ₁) < (E _c –E _f) (whereE _a,E _c andE _e = activation energies at band edge, fermi level and conduction band;W ₁ andW ₂ = activation energy for hopping). It was found that the density of states at the fermi levelN(E _f) increases with the decrease of silicon content. The results provided further evidence against thermal interpretations and thereby support electronic models of threshold switching for these glass systems.     

Performance evaluation for DRED discrete-time queueing network analytical model

Due to the rapid development in computer networks, congestion becomes a critical issue. Congestion usually occurs when the connection demands on network resources, i.e. buffer spaces, exceed the available ones. We propose in this paper a new discrete-time queueing network analytical model based on dynamic random early drop (DRED) algorithm to control the congestion in early stages. We apply our analytical model on two-queue nodes queueing network. Furthermore, we compare between the proposed analytical model and three known active queue management (AQM) algorithms, including DRED, random early detection (RED) and adaptive RED, in order to figure out which of them offers better quality of service (QoS). We also experimentally compare the queue nodes of the proposed analytical model and the three AQM methods in terms of different performance measures, including, average queue length, average queueing delay, throughput, packet loss probability, etc., aiming to determine the queue node that offers better performance.